Systemic immune indicators for predicting renal damage in newly diagnosed type 1 diabetic children

Abstract

BACKGROUND

Early kidney damage is a significant complication in children with newly diagnosed type 1 diabetes mellitus (T1DM). Systemic inflammation plays a key role in the development of diabetic nephropathy. Several inflammatory markers, including the systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), have been proposed as potential indicators of diabetic complications.

AIM

To investigate the association between SII, NLR, PLR, and early kidney damage in newly diagnosed T1DM children without pre-existing albuminuria, assessing their utility as predictive biomarkers.

METHODS

A longitudinal cohort study was conducted on 102 children aged 3-18 years with newly diagnosed T1DM [baseline urinary albumin-to-creatinine ratio (UACR) < 30 mg/g] recruited between January 2020 and June 2023. Participants were followed biannually for up to three years. Demographic, clinical, and laboratory data, including inflammatory markers (SII, NLR, PLR), were collected at baseline and follow-up. Logistic regression and receiver operating characteristic analyses were used to evaluate the predictive utility of these markers for early kidney damage, defined as UACR ≥ 30 mg/g.

RESULTS

SII emerged as a significant independent predictor of early kidney damage [odds ratio = 1.002, 95% confidence interval (CI): 1.0008-1.0033, P = 0.0016], with an area under the curve of 0.719 (95%CI: 0.612-0.826, P < 0.001). Using an SII threshold of ≥ 624.015 achieved a sensitivity of 59.6% and specificity of 92%. Combining SII with NLR and PLR improved predictive accuracy (area under the curve = 0.787), with sensitivity and specificity of 63.5% and 96%, respectively. Correlation analyses revealed significant associations between SII, metabolic markers (triglycerides, glycated hemoglobin), and UACR.

CONCLUSION

SII is a reliable biomarker for early kidney damage in T1DM children, offering high specificity for identifying at-risk patients. Combining SII with NLR and PLR enhances diagnostic precision, supporting its integration into clinical practice. Longitudinal monitoring of these markers may facilitate early interventions to mitigate renal complications in pediatric T1DM.

Key Words: Pediatric type 1 diabetes mellitus; Systemic immune inflammation index; Kidney injury; Urinary albumin-to-creatinine ratio; Inflammatory markers

Core Tip: This study highlights the predictive value of systemic inflammatory markers - systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio - for early kidney damage in children with newly diagnosed type 1 diabetes mellitus. SII emerged as a significant independent predictor of renal injury, with a high specificity threshold. The combination of SII with neutrophil-to-lymphocyte ratio or platelet-to-lymphocyte ratio further enhanced diagnostic accuracy. These simple, cost-effective markers provide valuable tools for early detection and intervention, paving the way for improved clinical strategies to prevent diabetic nephropathy in pediatric type 1 diabetes mellitus patients.

INTRODUCTION

Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by the destruction of insulin-producing beta cells in the pancreas, leading to absolute insulin deficiency. While the disease primarily affects glucose metabolism, it is increasingly recognized that systemic inflammation plays a significant role in the early complications observed in T1DM, particularly in renal dysfunction[1]. Chronic diabetic complications are major determinants of long-term health outcomes in children with diabetes. Among these, diabetic nephropathy is particularly significant, as it contributes to approximately 50% of cases of end-stage renal disease in this population[2]. Diabetic renal damage, however, remains the most prevalent and debilitating of these, underscoring the importance of early detection and management in preventing irreversible renal impairment. The identification of early biomarkers for renal damage is crucial for timely interventions to prevent the progression of diabetic nephropathy in children.

In recent years, various inflammatory markers have been implicated in the pathogenesis of T1DM and its complications. Early renal damage in T1DM has been increasingly recognized as being closely associated with systemic inflammation and oxidative stress[3,4]. Numerous studies have demonstrated that both systemic immune inflammation and localized inflammatory responses play critical roles in the pathogenesis of diabetic kidney damage, particularly in the early stages of the disease. Recent evidence suggests that inflammatory markers, such as those derived from routine blood tests, may serve as valuable indicators of renal injury in T1DM patients. Among these, the systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) have gained attention as potential biomarkers of systemic inflammation[5]. Blood cell counts, routinely obtained in clinical practice, provide a simple and cost-effective means to evaluate these inflammatory indices, making them highly relevant for early detection and intervention in diabetic nephropathy. These markers are easy to measure and have been associated with the severity of various inflammatory conditions, including metabolic disorders and cardiovascular diseases[6]. However, the specific role of these markers in predicting early kidney injury in children with newly diagnosed T1DM remains underexplored.

SII, calculated as the product of platelet count and neutrophil count divided by lymphocyte count, has been shown to correlate with inflammatory activity in several chronic diseases, including T2DM[7,8]. NLR and PLR are simple inflammatory indices derived from routine blood counts and have been used to predict systemic inflammation and complications in conditions such as hypertension, cardiovascular disease, and metabolic syndrome. Previous studies have suggested that these markers may be useful in predicting kidney involvement in adult populations with diabetes. However, there is limited data on their utility in pediatric T1DM, particularly in the context of early renal dysfunction. We hypothesize that systemic inflammatory markers, specifically SII, NLR, and PLR, are associated with early renal damage in newly diagnosed T1DM children. Furthermore, we propose that SII has superior predictive value compared to NLR and PLR. This study aims to investigate the longitudinal relationship between these inflammatory markers and early kidney injury, as assessed by the urinary albumin-to-creatinine ratio (UACR), in children with newly diagnosed T1DM who are free of albuminuria at baseline. In addition, we explore the correlation between these inflammatory markers and key metabolic parameters, such as glycated hemoglobin (HbA1c), lipid profiles, and renal function indices, to better understand the underlying pathophysiological mechanisms. By examining the predictive value of SII, NLR, and PLR, this research seeks to contribute to the development of effective monitoring and management strategies for early renal complications in pediatric T1DM patients.

MATERIALS AND METHODS

Study design and participants

This study utilized a longitudinal cohort design to investigate the relationship between systemic inflammatory markers and early renal injury in newly diagnosed T1DM children without pre-existing albuminuria. A total of 102 participants aged 3-18 years were recruited from the Department of Endocrinology and Genetic Metabolism at our hospital between January 2020 and June 2023. All participants met the diagnostic criteria outlined in the 2020 Chinese Children Expert Consensus on Standardized Diagnosis and Treatment of T1DM. Inclusion criteria were: (1) Newly diagnosed T1DM patients with a UACR < 30 mg/g at baseline; and (2) Written informed consent provided by guardians. Exclusion criteria included: (1) Acute injurious diseases or major surgeries within six months; (2) Autoimmune or other endocrine disorders; (3) Recent use of drugs affecting renal function (within three months); (4) Malignant tumors or existing liver/kidney diseases; and (5) Incomplete clinical data. The study was approved by the Jiangxi Children’s Hospital Ethics Committee (Approval No: JXSETYY-YXKY-20240117). Details of patient recruitment and study design are shown in Figure 1. The sample size was determined based on prior studies[9] that reported associations between inflammatory markers and renal outcomes in diabetic populations. A power analysis was conducted using an expected medium effect size (Cohen’s f² = 0.15) for multiple regression analyses, with an alpha level of 0.05 and a power of 0.8. This required a minimum sample size of 91 participants. To account for potential attrition, we recruited 102 participants.

Figure 1 Patient recruitment and study design flowchart. T1DM: Type 1 diabetes mellitus; BMI: Body mass index; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; TC: Total cholesterol; TG: Triglycerides; LDL: Low-density lipoprotein; BUN: Blood urea nitrogen; UACR: Urinary albumin-to-creatinine ratio; SII: Systemic immune inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio.

Demographic and clinical data collection

Demographic information such as age, gender, and family history of diabetes was recorded for each participant. Clinical parameters, including body mass index, systolic blood pressure (SBP), diastolic blood pressure (DBP), and HbA1c, were measured during the initial consultation. Blood pressure measurements were taken in a standardized manner using a calibrated sphygmomanometer after 5 minutes of rest.

Laboratory measurements

Fasting blood and midstream urine samples were collected and processed on the same day. Biochemical and inflammatory markers were measured using the following methods: (1) Routine blood parameters: Measured using the AU5831 Automatic Biochemical Analyzer (Beckman Coulter, United States), including total cholesterol (TC), triglycerides (TG), low-density lipoprotein, high-density lipoprotein, blood urea nitrogen, serum creatinine, alanine aminotransferase, and aspartate aminotransferase; (2) Insulin and C-peptide: Assayed using chemiluminescence immunoassays; (3) HbA1c: Quantified using the MQ6000 Analyzer (ARKRAY Inc., Japan) following National Glycohemoglobin Standardization Program guidelines; and (4) UACR: Calculated as urinary albumin (mg/L) divided by urinary creatinine (g/L), measured using the BA400 Automatic Specific Protein Analyzer (BioSystems, Spain). The estimated glomerular filtration rate (eGFR) was calculated using the Schwartz equation[10]: eGFR (mL/minute/1.73 m²) = K × height (cm) × 88.4/serum creatinine (μmol/L), where K is a constant dependent on age and sex (K = 0.413 for children ≥ 2 years). C-reactive protein (CRP), a key marker of systemic inflammation, was measured by high-sensitivity enzyme-linked immunosorbent assay. The SII, NLR, and PLR were calculated as follows: SII = platelet count × neutrophil count/lymphocyte count; NLR = neutrophil count/lymphocyte count; PLR = platelet count/lymphocyte count. Fasting insulin levels and C-peptide were measured by chemiluminescence immunoassay, and HbA1c was determined using high-performance liquid chromatography.

Longitudinal follow-up and endpoint assessment

Participants were followed every six months for up to three years, with repeat measurements of clinical and laboratory parameters. The primary endpoint was the development of significant albuminuria (UACR ≥ 30 mg/g). For the assessment of early kidney damage, participants were stratified into two groups based on their UACR: The “no damage” group (UACR < 30 mg/g) and the “damage” group (UACR ≥ 30 mg/g). Logistic regression analyses were conducted to evaluate the association between systemic inflammatory markers (SII, NLR, PLR) and the risk of early kidney damage. Longitudinal data were analyzed using logistic regression to assess the associations between baseline inflammatory markers and subsequent renal outcomes. Receiver operating characteristic curves determined the predictive value of these markers for early nephropathy. The results are presented as odds ratios with 95% confidence intervals (CIs), area under the curve (AUC) values, sensitivity, and specificity.

Statistical analysis

The data were analyzed using SPSS version 25 (IBM, Armonk, NY, United States). Descriptive statistics were used to summarize the demographic and clinical characteristics of the study population. Categorical variables were presented as frequencies and percentages, while continuous variables were expressed as means ± SD. Group comparisons were performed using one-way analysis of variance for continuous variables and χ²-tests for categorical variables. Post-hoc analyses with Bonferroni correction were conducted to identify intergroup differences when analysis of variance revealed statistical significance. Correlation analyses were performed using Pearson’s or Spearman’s correlation coefficients (depending on the distribution of the data) to explore associations between systemic inflammatory markers (SII, NLR, and PLR) and clinical parameters, including metabolic and renal biomarkers. A P value of < 0.05 was considered statistically significant. To identify predictors of systemic inflammation, multiple regression analyses were performed. The regression models included UACR, TG, HbA1c, and other relevant clinical variables as independent predictors of SII, NLR, and PLR. The results were expressed as estimates with 95%CIs.

RESULTS

General characteristics of study groups

The cohort included patients newly diagnosed with T1DM, free from albuminuria at baseline (UACR < 30 mg/g), and followed them longitudinally to evaluate the progression of renal complications. Baseline demographic and clinical characteristics are summarized in Table 1. No significant differences were observed in age distribution, gender, or family history of diabetes among the groups (P > 0.05). Body mass index, SBP, and DBP showed minor variations that were not statistically significant (P > 0.05). Significant differences were noted in TG and low-density lipoprotein levels (P < 0.001 and P = 0.002, respectively), suggesting metabolic alterations. Similarly, HbA1c, C-peptide, fasting insulin, and eGFR displayed significant intergroup differences (P < 0.001 for all), indicating their relevance in systemic inflammation and renal function. These findings align with the hypothesis that metabolic and inflammatory changes contribute to early renal dysfunction in children with T1DM.

Table 1 Comparison of general information and biochemical indices among four groups.

Indicator	NC group	NUA1b group	MUA1b group	CUA1b group	F/H/χ2	P value
Age, months	112 (106, 116)	119 (77, 150)	123 (79, 155)	131 (82, 145)	0.005	0.995
Family history	65 (30, 35)	50 (23, 27)	32 (15, 17)	20 (11, 9)	0.489	0.783
BMI, kg/m²	16.79 (15.01, 19.54)	16.40 (15.38, 18.95)	15.70 (14.75, 17.25)	17.15 (14.73, 19.65)	3.76	0.153
SBP, mmHg	104 ± 11	106 ± 13	108 ± 10	109 ± 15	0.473	0.625
DBP, mmHg	66 (59, 73)	64 (60, 72)	65 (60, 70)	67 (60, 78)	0.758	0.471
Blood urea, mmol/L	4.03 (3.45, 4.68)	4.39 (3.86, 5.88)	4.90 (4.08, 6.43)	5.20 (4.14, 7.97)a	3.348	0.187
Blood creatinine, μmol/L	37 (34, 42)	35 (30, 45)	33 (26, 44)	41 (35, 48)c	2.006	0.14
C-reactive protein, mg/L	4.1 (0.1, 5.7)	3.4 (0.0, 5.2)	2.4 (0.7, 4.4)	5.0 (2.3, 11.2)	1.002	0.059
ALT, U/L	29 (11, 37)	32 (24, 41)	36 (27, 51)	40 (22, 49)	0.177	0.838
AST, U/L	32 (20, 36)	31 (22, 46)	34 (26, 44)	41 (19, 53)	0.007	0.993
Total cholesterol, mmol/L	3.89 (3.34, 4.39)	4.31 (3.79, 5.08)a	4.91 (4.50, 5.41)a,b	5.68 (4.19, 6.65)a,b	8.708	0.013
Triglycerides, mmol/L	0.91 (0.71, 1.30)	1.05 (0.72, 2.15)a	1.70 (1.19, 2.91)a,b	2.83 (1.86, 3.79)a,b,c	20.631	< 0.001
HDL, mmol/L	1.35 (1.18, 1.53)	1.25 (1.07, 1.54)	1.23 (1.00, 1.47)	1.31 (1.06, 1.44)	0.244	0.784
LDL, mmol/L	2.06 (1.76, 2.51)	1.94 (1.60, 2.61)	2.98 (2.20, 3.50)a,b	2.44 (2.00, 3.31)b	6.593	0.002
C-peptide, nmol/L	0.80 (0.55, 1.10)	0.25 (0.09, 0.48)a	0.24 (0.15, 0.29)a	0.20 (0.12, 0.32)a	76.498	< 0.001
Insulin, mU/L	13.50 (8.50, 19.75)	2.46 (1.05, 5.62)a	2.05 (1.07, 3.63)a	2.79 (0.85, 4.22)a	100.35	< 0.001
eGFR, mL/minute/1.73 m²	194.36 ± 44.67	202.63 ± 74.54	207.92 ± 54.58	164.64 ± 33.71a,b,c	3.376	0.038
UACR, mg/g	8 (4, 14)	6 (3, 17)	76 (46, 158)a,b	565 (366, 820)a,b,c	85.232	< 0.001
HbA1c, %	5.0 (4.7, 5.3)	9.9 (7.0, 13.2)a	12.9 (11.4, 16.0)a,b	14.8 (13.4, 15.3)a,b	19.238	< 0.001
Systemic inflammation index, 109/L	278 (188.89, 331.47)	366.76 (254.14, 458.30)a	331.12 (202.53, 999.19)a	1459.92 (1214.27, 2838.30)a,b,c	37.354	< 0.001
Neutrophil-to-lymphocyte ratio	1.12 (0.69, 1.46)	1.42 (1.02, 1.95)a	1.80 (1.17, 2.59)a	5.54 (2.58, 9.55)a,b,c	24.207	< 0.001
Platelet-to-lymphocyte ratio	83.17 (59.83, 105.49)	104.05 (79.23, 115.83)a	113.51 (85.76, 149.95)a	130.13 (118.99, 195.80)a,b,c	20.53	< 0.001

^aP < 0.05, comparisons with the NC group.

^bP < 0.05, comparisons with the NUA1b group.

^cP < 0.05, comparisons with the MUA1b group.

BMI: Body mass index; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; eGFR: Estimated glomerular filtration rate; UACR: Urinary albumin-to-creatinine ratio; HbA1c: Glycated hemoglobin.

Correlations between systemic inflammatory markers and clinical parameters

Table 2 outlines the correlation between systemic inflammatory markers (SII, NLR, and PLR) and clinical parameters. UACR demonstrated a strong positive correlation with all three inflammatory indices (P < 0.001) (Table 2, Figure 2), highlighting its potential role in early renal injury. TG and HbA1c were positively correlated with SII and PLR (P = 0.006 and P = 0.044, respectively), while TC showed a moderate correlation with PLR (P = 0.006). SBP and DBP exhibited weaker associations, and eGFR showed non-significant negative trends with the inflammatory markers. CRP correlated moderately with SII but not with NLR or PLR. These results emphasize the interplay between systemic inflammation, lipid metabolism, and renal function in T1DM.

Figure 2 Correlation analysis between urinary albumin-to-creatinine ratio and inflammatory markers. A: Correlation between systemic immune-inflammatory index and urinary albumin-to-creatinine ratio (UACR); B: Correlation between neutrophil-to-lymphocyte ratio and UACR; C: Correlation between platelet-to-lymphocyte ratio and UACR. UACR: Urinary albumin-to-creatinine ratio; SII: Systemic immune inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio.

Table 2 Correlation of systemic immune inflammation index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio with various indicators.

Indicator	SII, r	SII, P	NLR, r	NLR, P	PLR, r	PLR, P
Age, months	0.12	0.228	0.055	0.585	0.084	0.4
SBP, mmHg	0.121	0.226	0.145	0.147	0.093	0.35
DBP, mmHg	0.119	0.232	0.200a	0.044	0.048	0.634
Blood urea, mmol/L	0.121	0.226	0.092	0.36	0.129	0.195
Blood creatinine, μmol/L	0.089	0.372	0.168	0.091	0.117	0.24
TC, mmol/L	0.135	0.177	0.104	0.297	0.268b	0.006
TG, mmol/L	0.270b	0.006	0.188	0.058	0.227a	0.022
HDL, mmol/L	-0.091	0.362	0.092	0.358	0.154	0.123
LDL, mmol/L	0.111	0.266	0.056	0.573	0.177	0.075
HbA1c, %	0.200a	0.044	0.129	0.197	0.176	0.077
eGFR, mL/minute/1.73 m²	-0.064	0.521	-0.183	0.066	-0.054	0.588
UACR, mg/g	0.552b	< 0.001	0.413b	< 0.001	0.300b	< 0.001
ALT, U/L	-0.076	0.451	0.045	0.65	-0.064	0.522
AST, U/L	0.012	0.908	-0.067	0.501	0.13	0.194
BMI, kg/m²	-0.008	0.933	-0.003	0.973	-0.031	0.755
C-peptide, nmol/L	0.063	0.529	-0.151	0.129	-0.084	0.401
Insulin, mU/L	-0.052	0.604	-0.036	0.716	0.045	0.653
Family history	0.086	0.391	0.032	0.753	0.054	0.592
C-reactive protein, mg/L	0.151	0.131	0.034	0.738	0.063	0.529

^aP < 0.05.

^bP < 0.01.

“r” denotes the correlation coefficient. “P” denotes the P value. SII: Systemic immune inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; TC: Total cholesterol; TG: Triglycerides; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; HbA1c: Glycated hemoglobin; eGFR: Estimated glomerular filtration rate; UACR: Urinary albumin-to-creatinine ratio; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; BMI: Body mass index.

Predictors of SII, NLR, and PLR

The regression analysis identified UACR as the strongest predictor of SII levels (Figure 3A, Supplementary Table 1). With an estimate of 1.955 (P < 0.0001), UACR was strongly associated with increased SII levels. For each unit increase in UACR, SII levels rose by approximately 1.955 units, underscoring the pivotal role of renal dysfunction in systemic inflammation. Although TG exhibited a positive estimate (80.01), its P value (P = 0.171) suggested no statistically significant contribution to predicting SII levels at conventional thresholds. A positive estimate (3.19) was observed for HbA1c, but it was not statistically significant (P = 0.902), indicating minimal predictive influence on SII levels in this model. Figure 3B presents the regression analysis for NLR levels. DBP showed a statistically significant positive association with NLR levels, with an estimate of 0.056 (P = 0.019). This indicates that each unit increase in DBP corresponds to a 0.056-unit rise in NLR, suggesting blood pressure’s role in systemic inflammation. A strong positive estimate (0.005, P < 0.001) confirmed UACR as a significant contributor to NLR, emphasizing the link between renal function and inflammation. Figure 3B and Supplementary Table 2 underscore the importance of monitoring DBP and UACR as indicators of inflammatory responses in newly diagnosed T1DM children. Figure 3C and Supplementary Table 3 provide insights into the factors influencing PLR levels. The intercept was highly significant (P < 0.0001), with an estimate of 98.25, suggesting a strong baseline relationship with PLR levels. TG exhibited a statistically significant positive association with PLR levels, supporting its relevance in systemic inflammation. UACR was also significantly associated with PLR, emphasizing the interplay between renal function and inflammatory responses. TC did not demonstrate a statistically significant impact on PLR levels in this analysis. These results highlight the significant roles of TG and UACR in influencing PLR, further confirming the close relationship between lipid metabolism, renal function, and systemic inflammation in the studied cohort. The findings from regression analyses across SII, NLR, and PLR underscore the importance of UACR as a consistent and significant predictor of systemic inflammation. Additionally, DBP and TG contribute variably to inflammatory indices, while other parameters such as HbA1c and TC exhibit limited predictive influence. These insights provide a foundation for further exploration of systemic inflammation’s role in early complications among children with newly diagnosed T1DM.

Figure 3 Predictors of systemic inflammatory indices in newly diagnosed type 1 diabetes mellitus children. A: Linear regression analysis and parameter covariance (heat map) of systemic immune inflammation index levels, showing urinary albumin-to-creatinine ratio (UACR) as the strongest predictor with a significant positive association (estimate = 1.955, P < 0.0001); B: Linear regression analysis and parameter covariance (heat map) of neutrophil-to-lymphocyte ratio levels, highlighting the significant positive contribution of diastolic blood pressure (estimate = 0.056, P = 0.019) and UACR (estimate = 0.005, P < 0.001); C: Linear regression analysis and parameter covariance (heat map) of platelet-to-lymphocyte ratio levels, demonstrating the strong baseline relationship with platelet-to-lymphocyte ratio and significant associations with triglycerides and UACR. SII: Systemic immune inflammation index; TG: Triglycerides; HbA1c: Glycated hemoglobin; DBP: Diastolic blood pressure; UACR: Urinary albumin-to-creatinine ratio; TC: Total cholesterol; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio.

Risk factors for early kidney damage in newly diagnosed T1DM children

Logistic regression analysis demonstrated that systemic inflammatory markers were significantly associated with the progression of albuminuria in children with T1DM. Among these markers, the SII emerged as the most significant predictor, with an odds ratio of 1.002 (95%CI: 1.0008-1.0033, P = 0.0016). However, NLR and PLR exhibited limited predictive utility when assessed individually (P > 0.05 for both) (Figure 4A). The predictive ability of SII was further validated through receiver operating characteristic analysis, which revealed an AUC of 0.719 (P < 0.001). This corresponds to a sensitivity of 59.6% and a high specificity of 92% at a threshold value of SII ≥ 624.015. These findings indicate that while SII may not detect all cases of early kidney damage (moderate sensitivity), its high specificity ensures that children identified above this threshold are highly likely to have or develop early renal injury. Clinically, this implies that SII can be a reliable tool for confirming high-risk cases, which may warrant close monitoring and early therapeutic intervention. Similarly, a PLR threshold of 127.51 demonstrated high specificity (94%) but lower sensitivity (53.8%), suggesting its utility in confirming, rather than screening, cases of early kidney damage. Combining multiple markers, such as SII with NLR or PLR, improved predictive accuracy. For instance, the combination of SII + NLR achieved an AUC of 0.787 with sensitivity and specificity of 63.5% and 96%, respectively. These results highlight the complementary role of these markers in providing a comprehensive assessment of systemic inflammation and renal risk (Table 3, Figure 4B). Using an SII threshold of ≥ 624.015 as a clinical cutoff provides a high degree of confidence in identifying children at risk of early kidney damage. In clinical decision-making, this threshold may be used to prioritize further diagnostic evaluations, such as urinary biomarkers or imaging studies, for children with elevated SII. Similarly, the specificity of the PLR threshold underscores its role in supporting diagnostic confirmation rather than initial screening.

Figure 4 Risk factors for early kidney damage in newly diagnosed type 1 diabetes mellitus children. A: Logistic regression analysis of systemic inflammatory markers (systemic immune inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio) as predictors of early kidney damage, stratified by urinary albumin-to-creatinine ratio (UACR, UACR < 30 mg/g vs UACR ≥ 30 mg/g); B: Receiver operating characteristic curve analysis for the predictive performance of systemic immune inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and their combinations in early kidney damage prediction. SII: Systemic immune inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; OR: Odds ratio; CI: Confidence interval.

Table 3 Predictive significance of biomarkers for early kidney damage in children with type 1 diabetes mellitus.

Indicator combination	AUC	95% confidence interval	P value	Threshold	Sensitivity, %	Specificity, %
SII	0.72	0.612-0.826	< 0.001	624.015	59.6	92
NLR	0.7	0.603-0.804	< 0.001	2.17	55.8	80
PLR	0.71	0.604-0.811	< 0.001	127.51	53.8	94
SII + NLR	0.77	0.676-0.869	< 0.001	0.645	55.8	98
SII + PLR	0.79	0.695-0.887	< 0.001	0.502	69.2	90
NLR + PLR	0.73	0.627-0.828	< 0.001	0.466	63.5	84
SII + NLR + PLR	0.79	0.690-0.883	< 0.001	0.604	63.5	96

AUC: Area under the curve; SII: Systemic immune inflammation index; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio.

DISCUSSION

T1DM is one of the most prevalent endocrine disorders in childhood, and its incidence has been rising globally in recent years. The development of microvascular complications in T1DM begins early in the disease course and can manifest within a few years of diagnosis, even in children[11]. Among these complications, diabetic kidney damage is of particular concern, as it significantly impacts long-term health outcomes. This study investigated the longitudinal association between systemic inflammatory markers - SII, NLR, and PLR - and the progression of early kidney damage in children newly diagnosed with T1DM. By following patients without albuminuria at baseline, we aimed to explore how inflammation contributes to the development of albuminuria over time. Our results demonstrated that elevated SII levels were strongly associated with progression to significant albuminuria, highlighting the potential of this marker as an early indicator of nephropathy risk.

Elevated inflammatory markers have been shown to be predictive of poor outcomes in both adult and pediatric populations. Systemic inflammation and oxidative stress are now well-established factors contributing to the pathogenesis of diabetic kidney disease[12]. Inflammatory pathways, including the activation of NOD-like receptor protein 3 inflammasomes and the release of pro-inflammatory cytokines such as interleukins and tumor necrosis factor, are implicated in renal structural damage in diabetes. These inflammatory mediators lead to glomerular mesangial cell proliferation, extracellular matrix deposition, and ultimately glomerulosclerosis, exacerbating renal albumin excretion and promoting renal fibrosis and tubular injury[13], supporting our observation of a moderate association between NLR and early kidney damage. The high glucose environment in diabetes exacerbates local and systemic inflammatory responses, leading to activation of inflammatory cells in the kidney, further contributing to glomerulosclerosis and tubulointerstitial fibrosis[14]. In addition, inflammatory processes can amplify oxidative stress, thereby worsening diabetic neuropathy. Neutrophils, lymphocytes, and platelets are central to both innate and adaptive immune responses and are critical players in the development of microvascular complications in T1DM[15].

The NLR, a marker of the balance between innate and adaptive immune responses, has gained attention as a potential biomarker of systemic inflammation in T1DM. Studies have shown that NLR correlates with disease severity in autoimmune diseases, infections, and malignancies, and is an independent prognostic marker of mortality and morbidity in various conditions[16,17]. The PLR has also emerged as a valuable marker of inflammation and platelet activation, which plays a key role in the pathogenesis of atherosclerosis and cardiovascular diseases[18]. Recent research suggests that elevated PLR levels are associated with increased systemic inflammation in conditions such as diabetes. The SII, a composite marker involving neutrophils, platelets, and lymphocytes, has been identified as a novel and comprehensive marker of systemic immune dysregulation. SII reflects the balance of inflammation and immunity more holistically compared to NLR and PLR alone, and it has been shown to be an independent prognostic factor in various chronic inflammatory diseases[19]. Wan et al[20] observed a positive correlation between serum NLR levels and the prevalence of diabetic kidney disease in diabetic patients, further corroborated by studies showing increased proteinuria and worsening renal function with rising NLR levels in diabetic populations[21]. Additionally, studies have reported higher levels of SII, NLR, and PLR in diabetic patients with microvascular complications, positioning these markers as important predictors of kidney damage and other diabetic complications[22]. However, our study differs from some previous work in terms of the specific inflammatory markers assessed. While many studies have focused on traditional inflammatory markers like CRP, we included SII, NLR, and PLR, which reflect more comprehensive aspects of systemic inflammation. This approach offers a more nuanced understanding of the inflammatory response in T1DM and its potential role in early kidney injury[23]. Furthermore, we found that SII demonstrated a higher predictive value for kidney damage compared to NLR and PLR, which contrasts with some studies that have suggested a more balanced contribution of these markers. Unlike cross-sectional studies, our longitudinal design allowed us to assess temporal changes in inflammatory markers and their impact on kidney function. This approach aligns with recommendations from recent studies, such as Altıncık et al[9], which emphasized the need for prospective designs to validate early biomarkers of diabetic nephropathy. By enrolling children at the onset of T1DM diagnosis and tracking changes over time, we addressed prior methodological limitations and provided a clearer picture of the dynamic relationship between systemic inflammation and renal outcomes.

Furthermore, the study found that elevated TG and TC levels, particularly in the microalbuminuria and macroalbuminuria groups, were positively correlated with PLR, highlighting the interplay between lipid metabolism and inflammation in T1DM. Inflammatory cells, including neutrophils and platelets, are known to interact with lipids and promote atherosclerosis, which can further aggravate renal damage through increased glomerular endothelial cell dysfunction and monocyte infiltration[24]. Studies have shown that in metabolic disorders like obesity and dyslipidemia, impaired adipocyte function leads to enhanced release of pro-inflammatory cytokines, such as tumor necrosis factor α and interleukin-6, which contribute to kidney injury in diabetes[25,26]. The findings from this study underscore the critical role of systemic inflammation in the pathogenesis of early renal damage in children with T1DM.

Despite the strengths, our study has several limitations. First, while our longitudinal design allows for temporal analysis, the relatively short follow-up period may not capture the full spectrum of nephropathy progression. Extending the follow-up duration in future studies will provide more robust insights into long-term outcomes. Second, while SII was identified as a significant predictor, the underlying molecular mechanisms were not explored. Future research should focus on elucidating the pathways linking systemic inflammation to renal injury, including the role of oxidative stress and endothelial dysfunction. Additionally, while our study provides strong evidence of the relationship between systemic inflammation and kidney function, it did not explore the underlying mechanisms of inflammation in detail. Future studies should aim to elucidate the pathways by which inflammation contributes to renal injury in T1DM, such as the role of oxidative stress, endothelial dysfunction, and immune dysregulation. Furthermore, while we assessed several potential predictors of systemic inflammation, other factors such as genetic predisposition and environmental influences were not considered. Exploring these factors could provide a more comprehensive understanding of the interplay between systemic inflammation and early kidney injury.

CONCLUSION

This study highlights the critical role of systemic inflammation, as reflected by SII, in the progression of early kidney damage in children with newly diagnosed T1DM. Our findings underscore the potential of SII as a reliable and practical biomarker for identifying at-risk individuals. Future longitudinal research with extended follow-up and expanded biomarker panels will be essential to validate these findings and advance our understanding of diabetic nephropathy pathogenesis.